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International Journal of Frontiers in Engineering Technology, 2021, 3(5); doi: 10.25236/IJFET.2021.030507.

Real Time Neural Network Path Planning Algorithm for Robot


Wenzheng Du, Quanmao Zhang, Zhenxin He and Xin Wang

Corresponding Author:
Zhenxin He

Rocket Force University of Engineering, Xi’an 710025, China


The emergence of robots not only changed the traditional industrial production mode, but also greatly promoted the progress of social civilization. Whether in daily life or in industrial production practice, the technical level of robots is improving every day, which emphasizes the high level of national science and technology. Robot path planning technology is an important part of robot research. The purpose of this paper is to focus on the research of robot path planning algorithm, learning and in-depth development. This paper introduces the discovery and communication of robot real-time neural network planning algorithm in machine learning, and analyzes and studies it. In order to study the robot real-time neural network path planning algorithm, through the experimental comparison of different algorithms, focus on exploring the effect of different algorithms on path planning. The research results show that the algorithm speed of robot on real-time network path is 24% higher than that of normal network path algorithm, and can be increased to 30% after deep mining, and the efficiency of machine algorithm can be increased to 35% under more complete algorithm. Therefore, the robot real-time neural network path planning algorithm can complete the task more efficiently.


Path Planning, Algorithm Speed, Computing Advantage, Deep Mining

Cite This Paper

Wenzheng Du, Quanmao Zhang, Zhenxin He, Xin Wang. Real Time Neural Network Path Planning Algorithm for Robot. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 5: 53-63. https://doi.org/10.25236/IJFET.2021.030507.


[1] J.-H. Park, U.-Y. Huh. Local Path Planning for Mobile Robot Using Artificial Neural Network - Potential Field Algorithm[J]. Transactions of the Korean Institute of Electrical Engineers, 2015, 64(10):1479-1485.

[2] Zhou C, Jin M H , Liu Y C . Singularity Robust Path Planning for Real Time Base Attitude Adjustment of Free-floating Space Robot[J]. International Journal of Automation and Computing, 2017, 14(2):53-62.

[3] Pradhan B , Nandi A , Hui N B . A Novel Hybrid Neural Network-Based Multirobot Path Planning with Motion Coordination[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2):1319-1327.

[4] Kim H K , Sim H S , Hwang W J . A Study on a Path Planning and Real-Time Trajectory Control of Autonomous Travelling Robot for Unmanned FA[J]. Journal of the Korean Society of Industry Convergence, 2016, 19(2):75-80.

[5] Wang Z , Li H , Zhang X . Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach[J]. Automation in Construction, 2019, 97(7):220-228.

[6] Pradhan B , Nandi A , Hui N B . A Novel Hybrid Neural Network-Based Multirobot Path Planning with Motion Coordination[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2):1319-1327.

[7] Woo M H , Lee S H , Cha H M . A study on the optimal route design considering time of mobile robot using recurrent neural network and reinforcement learning[J]. Journal of Mechanical ence and Technology, 2018, 32(10):4933-4939.

[8] Atta-ur-Rahman, Dash S . Data Mining for Student's Trends Analysis Using Apriori Algorithm[J]. International Journal of Control Theory & Applications, 2017, 10(18):107-115.

[9] GUO Yue,LI Xiao-wen.Path Planning Application of Palletizing Robot Based on Genetic Algorithms[J]. Packaging Engineering, 2019, 40(21):167-172.

[10] Rout A , Bbvl D , Biswal B B. Optimal Trajectory Generation of an Industrial Welding Robot with Kinematic and Dynamic Constraints[J]. Industrial Robot an International Journal, 2019, 47(1):68-75.

[11] Sun B , Zhu D , Tian C. Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming[J]. IEEE Transactions on Cognitive and Developmental Systems, 2019, 11(1):73-84.

[12] Alotaibi E T S , Al-Rawi H . A complete multi-robot path-planning algorithm[J]. Autonomous Agents and Multi-Agent Systems, 2018, 32(5):1-48.

[13] Pandey A , Parhi D R . Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm[J]. Defence Technology, 2017, 13( 1):47-58.

[14] Yu J . Average Case Constant Factor Time and Distance Optimal Multi-Robot Path Planning in Well-Connected Environments[J]. Autonomous Robots, 2020, 44(3):469-483.

[15] Parhi D R , Mohanta J C . Mobile Robot Path Planning and Tracking using AI Techniques[J]. International Journal of Computational I, 2017, 12(1):1-26.

[16] Zhao R , Lee D H , Lee H K . Mobile Robot Navigation using Optimized Fuzzy Controller by Genetic Algorithm[J]. International Journal of Fuzzy Logic & Intelligent Systems, 2015, 15(1):12-19.

[17] Berger J , Lo N , Boukhtouta A l. An information theoretic based integer linear programming approach for the discrete search path planning problem[J]. Optimization Letters, 2015, 9(8):1585-1607.

[18] Daugherty G , Reveliotis S , Mohler G . Optimized Multiagent Routing for a Class of Guidepath-Based Transport Systems[J]. IEEE Transactions on Automation Science & Engineering, 2019, 16(1):363-381.

[19] Shieh M Y . Visual Servo Strategy for Robot Soccer Systems[J]. Sensors & Materials, 2018, 30(2):893-906.

[20] Zhu D , Liu Y , Sun B . Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm[J]. The Journal of Navigation, 2018, 71(2):482-496.

[21] Jiajia Chen, Wuhua Jiang, Pan Zhao. A path planning method of anti-jamming ability improvement for autonomous vehicle navigating in off-road environments[J]. Industrial Robot, 2017, 44(4):406-415.

[22] Duguleana M , Mogan G . Neural networks based reinforcement learning for mobile robots obstacle avoidance[J]. Expert Systems with Application, 2016, 62(15):104-115.

[23] Pradhan B , Nandi A , Hui N B. A Novel Hybrid Neural Network-Based Multirobot Path Planning with Motion Coordination[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2):1319-1327.

[24] Ling S . A Real-Time Collision-Free Path Planning of a Rust Removal Robot Using an Improved Neural Network[J]. Journal of Shanghai Jiaotong University (ence), 2017, 22(5):633-640.

[25] Zhu D Q , Sun B , Li L . Algorithm for AUV's 3-D path planning and safe obstacle avoidance based on biological inspired model[J]. Control & Decision, 2015, 30(5):798-806.